scholarly journals Application of a WiFi/Geomagnetic Combined Positioning Method in a Single Access Point Environment

2021 ◽  
Vol 2021 ◽  
pp. 1-8
Author(s):  
Hongbin Pan ◽  
Yang Xiang ◽  
Jian Xiong ◽  
Yifan Zhao ◽  
Ziwei Huang ◽  
...  
Keyword(s):  
Nearest Neighbor ◽  
Detection Method ◽  
Access Point ◽  
Combination Method ◽  
Data Set ◽  
Positioning Accuracy ◽  
Multipath Effects ◽  
Positioning Method ◽  
Fingerprint Database ◽  
Mismatch Rate

Because of the particularity of urban underground pipe corridor environment, the distribution of wireless access points is sparse. It causes great interference to a single WiFi positioning method or geomagnetic method. In order to meet the positioning needs of daily inspection staff, this paper proposes a WiFi/geomagnetic combined positioning method. In this combination method, firstly, the collected WiFi strength data was filtered by outlier detection method. Then, the filtered data set was used to construct the offline fingerprint database. In the following positioning operation, the classical k -nearest neighbor algorithm was firstly used for preliminary positioning. Then, a standard circle was constructed based on the points obtained by the algorithm and the actual coordinate points. The diameter of the standard circle was the error, and the geomagnetic data were used for more accurate positioning in this circle. The method reduced the WiFi mismatch rate caused by multipath effects and improved positioning accuracy. Finally, a positioning accuracy experiment was performed in a single AP distribution environment that simulates a pipe corridor environment. The results proves that the WiFi/geomagnetic combined positioning method proposed in this paper is superior to the traditional WiFi and geomagnetic positioning methods in terms of positioning accuracy.

scholarly journals A novel method of adaptive weighted K-nearest neighbor fingerprint indoor positioning considering user’s orientation

2018 ◽  
Vol 14 (6) ◽  
pp. 155014771878588 ◽  
Author(s):  
Jingxue Bi ◽  
Yunjia Wang ◽  
Xin Li ◽  
Hongji Cao ◽  
Hongxia Qi ◽  
...  
Keyword(s):  
Human Body ◽  
Nearest Neighbor ◽  
Reference Points ◽  
Average Error ◽  
K Nearest Neighbor ◽  
Positioning Accuracy ◽  
Factors Affecting ◽  
Positioning Method ◽  
Hybrid Distance ◽  
Fingerprint Database

There are many factors affecting Wi-Fi signal in indoor environment, among which the human body has an important impact. And, its characteristic is related to the user’s orientation. To eliminate positioning errors caused by user’s human body and improve positioning accuracy, this study puts forward an adaptive weighted K-nearest neighbor fingerprint positioning method considering the user’s orientation. First, the orientation fingerprint database model is proposed, which includes the position, orientation, and the sequence of mean received signal strength indicator at each reference point. Second, the fuzzy c-means algorithm is used to cluster orientation fingerprint database taking the hybrid distance of the signal domain and position domain as the clustering feature. Finally, the proposed adaptive algorithm is developed to select K-reference points by matching operation, to remove the reference points with larger signal-domain distances, minimum and maximum coordinate values, and calculate the weighted mean coordinates of the remaining reference points for positioning results. The experimental results show that the average error decreases by 0.7 m, and the root mean square error decreases to about 1.3 m by the proposed technique. And, we conclude that the proposed adaptive weighted K-nearest neighbor fingerprint positioning method can improve positioning accuracy.

scholarly journals An Improved Bluetooth Indoor Positioning Method Using Dynamic Fingerprint Window

Sensors ◽  
2020 ◽  
Vol 20 (24) ◽  
pp. 7269
Author(s):  
Ling Ruan ◽  
Ling Zhang ◽  
Tong Zhou ◽  
Yi Long
Keyword(s):  
Large Scale ◽  
Nearest Neighbor ◽  
Indoor Positioning ◽  
K Nearest Neighbor ◽  
Positioning Accuracy ◽  
Local Clustering ◽  
Positioning Method ◽  
K Nearest Neighbor Algorithm ◽  
Space Range ◽  
Fingerprint Data

The weighted K-nearest neighbor algorithm (WKNN) is easily implemented, and it has been widely applied. In the large-scale positioning regions, using all fingerprint data in matching calculations would lead to high computation expenses, which is not conducive to real-time positioning. Due to signal instability, irrelevant fingerprints reduce the positioning accuracy when performing the matching calculation process. Therefore, selecting the appropriate fingerprint data from the database more quickly and accurately is an urgent problem for improving WKNN. This paper proposes an improved Bluetooth indoor positioning method using a dynamic fingerprint window (DFW-WKNN). The dynamic fingerprint window is a space range for local fingerprint data searching instead of universal searching, and it can be dynamically adjusted according to the indoor pedestrian movement and always covers the maximum possible range of the next positioning. This method was tested and evaluated in two typical scenarios, comparing two existing algorithms, the traditional WKNN and the improved WKNN based on local clustering (LC-WKNN). The experimental results show that the proposed DFW-WKNN algorithm enormously improved both the positioning accuracy and positioning efficiency, significantly, when the fingerprint data increased.

scholarly journals An RSSD-Based Fingerprint Positioning Method for Detection of an Unknown Radio Transmitter Using WLS and Factor Graph

2018 ◽  
Vol 2018 ◽  
pp. 1-11
Author(s):  
Liyang Zhang ◽  
Taihang Du ◽  
Chundong Jiang
Keyword(s):  
Nearest Neighbor ◽  
Access Point ◽  
Reference Points ◽  
Least Square ◽  
Factor Graph ◽  
K Nearest Neighbor ◽  
Positioning Accuracy ◽  
Radio Transmitter ◽  
Error Weight ◽  
The Impact

Realizing accurate detection of an unknown radio transmitter (URT) has become a challenge problem due to its unknown parameter information. A method based on received signal strength difference (RSSD) fingerprint positioning technique and using factor graph (FG) has been successfully developed to achieve the localization of an URT. However, the RSSD-based FG model is not accurate enough to express the relationship between the RSSD and the corresponding location coordinates since the RSSD variances of reference points are different in practice. This paper proposes an enhanced RSSD-based FG algorithm using weighted least square (WLS) to effectively reduce the impact of RSSD measurement variance difference on positioning accuracy. By the use of stochastic RSSD errors between the measured value and the estimated value of the selected reference points, we utilize the error weight matrix to establish a new WLSFG model. Then, the positioning process of proposed RSSD-WLSFG algorithm is derived with the sum-product principle. In addition, the paper also explores the effects of different access point (AP) numbers and grid distances on positioning accuracy. The simulation experiment results show that the proposed algorithm can obtain the best positioning performance compared with the conventional RSSD-based K nearest neighbor (RSSD-KNN) and RSSD-FG algorithms in the case of different AP numbers and grid distances.

scholarly journals Near Relation-Based Indoor Positioning Method under Sparse Wi-Fi Fingerprints

2020 ◽  
Vol 9 (12) ◽  
pp. 714
Author(s):  
Yankun Wang ◽  
Renzhong Guo ◽  
Weixi Wang ◽  
Xiaoming Li ◽  
Shengjun Tang ◽  
...  
Keyword(s):  
Interpolation Method ◽  
Indoor Positioning ◽  
Kriging Interpolation ◽  
Fingerprint Matching ◽  
Positioning Accuracy ◽  
Position Information ◽  
Kriging Interpolation Method ◽  
Additional Equipment ◽  
Positioning Method ◽  
Fingerprint Database

Indoor positioning is of great importance in the era of mobile computing. Currently, considerable focus has been on RSS-based locations because they can provide position information without additional equipment. However, this method suffers from two challenges: (1) fingerprint ambiguity and (2) labour-intensive fingerprint collection. To overcome these drawbacks, we provide a near relation-based indoor positioning method under a sparse Wi-Fi fingerprint. To effectively obtain the fingerprint database, certain interpolation methods are used to enrich sparse Wi-Fi fingerprints. A near relation boundary is provided, and Wi-Fi fingerprints are constrained to this region to reduce fingerprint ambiguity, which can also improve the efficiency of fingerprint matching. Extensive experiments show that the kriging interpolation method performs well, and a positioning accuracy of 2.86 m can be achieved with a near relation under a 1 m interpolation density.

scholarly journals An Improved Indoor Positioning Method Based on Nearest Neighbor Interpolation

2021 ◽  
Vol 6 (1) ◽  
pp. 1
Author(s):  
Yonghao Zhao
Keyword(s):  
Nearest Neighbor ◽  
Indoor Positioning ◽  
Location Information ◽  
Positioning Error ◽  
Positioning Accuracy ◽  
Indoor Location ◽  
Sampling Points ◽  
Sampling Cost ◽  
Positioning Method ◽  
Positioning Algorithm

Nowadays, people’s demand for indoor location information is more and more, which continuously promotes the development of indoor positioning technology. In the field of indoor positioning, fingerprint based indoor positioning algorithm still accounts for a large proportion. However, the operation of this method in the offline stage is too cumbersome and time-consuming, which makes its disadvantages obvious, and requires a lot of manpower and time to sample and maintain. Therefore, in view of this phenomenon, an improved algorithm based on nearest neighbor interpolation is designed in this paper, which reduces the measurement of actual sampling points when establishing fingerprint map. At the same time, some simulation points are added to expand fingerprint map, so as to ensure that the positioning error will not become larger or even better. Experimental results show that this method can further improve the positioning accuracy while saving the sampling cost.

scholarly journals An Adaptive Weighted KNN Positioning Method Based on Omnidirectional Fingerprint Database and Twice Affinity Propagation Clustering

Sensors ◽  
2018 ◽  
Vol 18 (8) ◽  
pp. 2502 ◽  
Author(s):  
Jingxue Bi ◽  
Yunjia Wang ◽  
Xin Li ◽  
Hongxia Qi ◽  
Hongji Cao ◽  
...  
Keyword(s):  
Nearest Neighbor ◽  
Great Influence ◽  
Weighted Average ◽  
Affinity Propagation ◽  
Cluster Center ◽  
K Nearest Neighbor ◽  
K Value ◽  
Affinity Propagation Clustering ◽  
Positioning Method ◽  
Fingerprint Database

The human body has a great influence on Wi-Fi signal power. A fixed K value leads to localization errors for the K-nearest neighbor (KNN) algorithm. To address these problems, we present an adaptive weighted KNN positioning method based on an omnidirectional fingerprint database (ODFD) and twice affinity propagation clustering. Firstly, an OFPD is proposed to alleviate body’s sheltering impact on signal, which includes position, orientation and the sequence of mean received signal strength (RSS) at each reference point (RP). Secondly, affinity propagation clustering (APC) algorithm is introduced on the offline stage based on the fusion of signal-domain distance and position-domain distance. Finally, adaptive weighted KNN algorithm based on APC is proposed for estimating user’s position during online stage. K initial RPs can be obtained by KNN, then they are clustered by APC algorithm based on their position-domain distances. The most probable sub-cluster is reserved by the comparison of RPs’ number and signal-domain distance between sub-cluster center and the online RSS readings. The weighted average coordinates in the remaining sub-cluster can be estimated. We have implemented the proposed method with the mean error of 2.2 m, the root mean square error of 1.5 m. Experimental results show that our proposed method outperforms traditional fingerprinting methods.

scholarly journals An Efficient Indoor Positioning Method Based on Wi-Fi RSS Fingerprint and Classification Algorithm

Sensors ◽  
2021 ◽  
Vol 21 (10) ◽  
pp. 3418
Author(s):  
Balaji Ezhumalai ◽  
Moonbae Song ◽  
Kwangjin Park
Keyword(s):  
Low Cost ◽  
Clustering Algorithms ◽  
Access Point ◽  
Indoor Positioning ◽  
Reference Points ◽  
Indoor Environments ◽  
Positioning Accuracy ◽  
Signal Distribution ◽  
Positioning Method ◽  
Handling Method

Wi-Fi received signal strength (RSS) fingerprint-based indoor positioning has been widely used because of its low cost and universality advantages. However, the Wi-Fi RSS is greatly affected by multipath interference in indoor environments, which can cause significant errors in RSS observations. Many methods have been proposed to overcome this issue, including the average method and the error handling method, but these existing methods do not consider the ever-changing dynamics of RSS in indoor environments. In addition, traditional RSS-based clustering algorithms have been proposed in the literature, but they make clusters without considering the nonlinear similarity between reference points (RPs) and the signal distribution in ever-changing indoor environments. Therefore, to improve the positioning accuracy, this paper presents an improved RSS measurement technique (IRSSMT) to minimize the error of RSS observation by using the number of selected RSS and its median values, and the strongest access point (SAP) information-based clustering technique, which groups the RPs using their SAP similarity. The performance of this proposed method is tested by experiments conducted in two different experimental environments. The results reveal that our proposed method can greatly outperform the existing algorithms and improve the positioning accuracy by 89.06% and 67.48%, respectively.

scholarly journals A Wireless Fingerprint Positioning Method Based on Wavelet Transform and Deep Learning

2021 ◽  
Vol 10 (7) ◽  
pp. 442
Author(s):  
Da Li ◽  
Zhao Niu
Keyword(s):  
Wavelet Transform ◽  
Positioning System ◽  
Fingerprint Image ◽  
Positioning Accuracy ◽  
Location Services ◽  
Multiple Data ◽  
Signal Features ◽  
Outdoor Environments ◽  
Positioning Method ◽  
Fingerprint Database

As the demand for location services increases, research on location technology has aroused great interest. In particular, signal-based fingerprint location positioning technology has become a research hotspot owing to its high positioning performance. In general, the received signal strength indicator (RSSI) will be used as a location feature to build a fingerprint database. However, at different locations, this feature distinction may not be obvious, resulting in low positioning accuracy. Considering the wavelet transform can get valuable features from the signals, the long-term evolution (LTE) signals were converted into wavelet feature images to construct the fingerprint database. To fully extract the signal features, a two-level hierarchical structure positioning system is proposed to achieve satisfactory positioning accuracy. A deep residual network (ResNet) rough locator is used to learn useful features from the wavelet feature fingerprint image database. Then, inspired by the transfer learning idea, a fine locator based on multilayer perceptron (MLP) is leveraged to further learn the features of the wavelet fingerprint image to obtain better localization performance. Additionally, multiple data enhancement techniques were adopted to increase the richness of the fingerprint dataset, thereby enhancing the robustness of the positioning system. Experimental results indicate that the proposed system leads to improved positioning performance in outdoor environments.

Information Filter-Assisted Indoor Bluetooth Positioning

2014 ◽  
pp. 162-177
Author(s):  
Liang Chen ◽  
Heidi Kuusniemi ◽  
Yuwei Chen ◽  
Ling Pei ◽  
Jingbin Liu ◽  
...  
Keyword(s):  
Field Tests ◽  
Signal Strength ◽  
Position Estimation ◽  
Indoor Navigation ◽  
Filter Method ◽  
Positioning Accuracy ◽  
Information Filter ◽  
Positioning Method ◽  
Wireless Positioning ◽  
Fingerprint Database

This chapter studies wireless positioning using a network of Bluetooth signals. Fingerprints of Received Signal Strength Indicators (RSSI) are used for localization. Due to the relatively long interval between the available consecutive Bluetooth signal strength measurements, the authors applied an information filter method with speed detection, which combines the estimation information from the RSSI measurements with the prior information from the motion model. Speed detection is assisted to correct the outliers of position estimation. The field tests show the effectiveness of the information filter-assisted positioning method, which improves the horizontal positioning accuracy of indoor navigation by about 17% compared to the static fingerprinting positioning method, achieving a 4.2 m positioning accuracy on the average, and about 16% improvement compared to the point Kalman filter. In RSSI fingerprinting localization, building a fingerprint database is usually time-consuming and labour-intensive. In the final section, a self-designed autonomous SLAM robot platform is introduced to be able to carry out the Bluetooth RSS data collecting.

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